grouped analysis

Super Grouped results

  ggplotly(
ggplot(filter(all_pronouns,total_num_com_in_sub < 100, num_til_quit > 6, post_num < 100)) +
  #geom_point(aes(post_num,contains_we,color=total_num_com_in_sub))+
  geom_smooth(aes(post_num,contains_i,color=total_num_com_in_sub))
)
`geom_smooth()` using method = 'loess' and formula 'y ~ x'

FINAL 3

all_pronouns <- pronoun_by_post_num %>%
#filter(post_num > 0, total_num_com_in_sub > 100), 
mutate(
  num_til_quit = (total_num_com_in_sub - post_num)
) %>% filter(num_til_quit < 5) %>% mutate(num_til_quit = as.character(num_til_quit), contains_wepct = contains_we*100)
ggplot(all_pronouns)+
  geom_point(aes(num_til_quit,contains_we,color = total_num_com_in_sub))

  library(RColorBrewer)
#ggplotly(
ggplot(filter(all_pronouns,  post_num>5), aes(post_num,contains_wepct,group=num_til_quit, color = num_til_quit)) +
  geom_point()+
  geom_smooth(se=FALSE)+ theme_classic(base_size = 15) +
   labs(title = "Collective Pronoun Use in Users' last 5 Comments in a Subreddit" ,
       subtitle = "Comments Grouped by Total Number of Comments in a Subreddit and Remaining Number of comments until last comment\nIncludes all comments from all subreddits where the author has at least 5 comments in that subreddit",
       caption = "") + xlab("Average of all authors' Nth comment in subreddit X") + ylab("Percentage of comments which contain 'We'") +
       scale_color_brewer(palette="Paired")
#)
ggsave("final3.png")
Saving 7.01 x 4.33 in image

ggplotly(
ggplot(filter(all_pronouns,total_num_com_in_sub > 15, total_num_com_in_sub < 400,num_til_quit > 5)) +
  geom_smooth(aes(post_num,ZNcontains_we, color = "We")) +
  geom_smooth(aes(post_num,ZNcontains_i, color = "I")) +
geom_smooth(aes(post_num,ZNcontains_us,color = "Us"  ))+
geom_smooth(aes(post_num,ZNcontains_they,color = "They"))+
geom_smooth(aes(post_num,ZNcontains_them,color = "Them"))+
geom_smooth(aes(post_num,ZNcontains_you,color = "You" ))+
geom_smooth(aes(post_num,ZNcontains_me,color = "Me"  ))
  
)
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
`geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
---
title: "02_comments_number_analysis"
output: html_notebook
---
#grouped analysis
```{r}
library(tidyverse)
pronoun_by_post_num %>%
  group_by(post_num, total_num_com_in_sub) %>% 
  summarise(
    #agg_author = sum(num_authors),
    agg_i      = sum(num_authors*contains_i     )/sum(num_authors),
    agg_me     = sum(num_authors*contains_me    )/sum(num_authors),
    agg_we     = sum(num_authors*contains_we    )/sum(num_authors),
    agg_us     = sum(num_authors*contains_us    )/sum(num_authors),
    agg_they   = sum(num_authors*contains_they  )/sum(num_authors),
    agg_them   = sum(num_authors*contains_them  )/sum(num_authors),
    agg_you    = sum(num_authors*contains_you   )/sum(num_authors)
  ) %>% filter(post_num<52)%>%
ggplot() +
  geom_point(aes(post_num,agg_you,color=total_num_com_in_sub))
  #facet_wrap(~topic_level1)
```
##Super Grouped results
```{r}

all_pronouns <- pronoun_by_post_num %>%
#filter(post_num > 0, total_num_com_in_sub > 100), 
mutate(
  num_til_quit = total_num_com_in_sub - post_num
)

library(plotly)

ggplotly(
ggplot(filter(all_pronouns, num_til_quit < 10), aes(post_num,contains_you,group=num_til_quit, color = num_til_quit)) +
  #geom_point()+
  geom_smooth(se = FALSE)
)
  ggplotly(
ggplot(filter(all_pronouns,total_num_com_in_sub < 100, num_til_quit > 6, post_num < 100)) +
  #geom_point(aes(post_num,contains_we,color=total_num_com_in_sub))+
  geom_smooth(aes(post_num,contains_i,color=total_num_com_in_sub))
)
  
  ggplotly(
ggplot(filter(all_pronouns,total_num_com_in_sub < 50, post_num < 20)) +
  #geom_point(aes(post_num,contains_we,color=total_num_com_in_sub))+
  geom_point(aes(post_num,contains_we,color=total_num_com_in_sub))
)
  
  ggplotly(
ggplot(filter(all_pronouns,total_num_com_in_sub < 500, post_num < 200)) +
  #geom_point(aes(post_num,contains_we,color=total_num_com_in_sub))+
  geom_point(aes(total_num_com_in_sub,contains_we,z = post_num))
  #geom_smooth(aes(total_num_com_in_sub,contains_we,z = post_num),se = FALSE)
)

  filter(all_pronouns,total_num_com_in_sub < 500, post_num < 1000) %>%
  plot_ly(x=.$post_num, z=.$contains_i, y=.$total_num_com_in_sub, type="scatter3d", mode="markers", color = .$contains_we)
  
  filter(all_pronouns,total_num_com_in_sub < 500, post_num < 1000, num_til_quit < 300) %>%
  plot_ly(x=.$post_num, z=.$contains_we, y=.$num_til_quit, type="scatter3d", mode="markers", color = .$contains_we)
```
##FINAL 3
```{r}
all_pronouns <- pronoun_by_post_num %>%
#filter(post_num > 0, total_num_com_in_sub > 100), 
mutate(
  num_til_quit = (total_num_com_in_sub - post_num)
) %>% filter(num_til_quit < 5) %>% mutate(num_til_quit = as.character(num_til_quit), contains_wepct = contains_we*100)

ggplot(all_pronouns)+
  geom_point(aes(num_til_quit,contains_we,color = total_num_com_in_sub))

  library(RColorBrewer)
#ggplotly(
ggplot(filter(all_pronouns,  post_num>5), aes(post_num,contains_wepct,group=num_til_quit, color = num_til_quit)) +
  geom_point()+
  geom_smooth(se=FALSE)+ theme_classic(base_size = 15) +
   labs(title = "Collective Pronoun Use in Users' last 5 Comments in a Subreddit" ,
       subtitle = "Comments Grouped by Total Number of Comments in a Subreddit and Remaining Number of comments until last comment\nIncludes all comments from all subreddits where the author has at least 5 comments in that subreddit",
       caption = "") + xlab("Average of all authors' Nth comment in subreddit X") + ylab("Percentage of comments which contain 'We'") +
       scale_color_brewer(palette="Paired")
#)
ggsave("final3.png")
```




```{r}


start_points_all_nums <- all_pronouns %>%
  filter(post_num  == 1) %>%
  group_by(total_num_com_in_sub) %>%
  summarise(
    offset_i           = mean(contains_i   ),
    offset_me          = mean(contains_me  ),
    offset_we          = mean(contains_we  ),
    offset_us          = mean(contains_us  ),
    offset_they        = mean(contains_they),
    offset_them        = mean(contains_them),
    offset_you         = mean(contains_you ),
    offset_average_use = mean(contains_i+contains_me+contains_them+contains_we+
                                contains_us+contains_they+contains_you)/7
  )

all_pronouns <- all_pronouns %>%
  left_join(start_points_all_nums, by = c( "total_num_com_in_sub" = "total_num_com_in_sub")) %>%
  
      #   !subreddit %in% c("chemicalreactiongifs", "personalfinance")) %>%
        #  subreddit %in% c("The_Donald", "LateStageCapitalism")
        #) %>%
  arrange(sort(post_num)) %>%
  group_by( total_num_com_in_sub) %>%
  mutate( 
    ZNcontains_i    = 100*(contains_i   /(offset_i   )-1),
    ZNcontains_me   = 100*(contains_me  /(offset_me  )-1),
    ZNcontains_we   = 100*(contains_we  /(offset_we  )-1),
    ZNcontains_us   = 100*(contains_us  /(offset_us  )-1),
    ZNcontains_they = 100*(contains_they/(offset_they)-1),
    ZNcontains_them = 100*(contains_them/(offset_them)-1),
    ZNcontains_you  = 100*(contains_you  /(offset_you )-1)
    ) %>% ungroup()

filter(all_pronouns,total_num_com_in_sub < 500, post_num < 1000, num_til_quit < 300) %>%
  plot_ly(x=.$post_num, z=.$ZNcontains_we, y=.$total_num_com_in_sub, type="scatter3d", mode="markers", color = .$ZNcontains_we)


ggplotly(
ggplot(filter(all_pronouns,total_num_com_in_sub > 15, total_num_com_in_sub < 100,num_til_quit > 5)) +
  geom_smooth(aes(post_num,ZNcontains_we,group=total_num_com_in_sub,color = total_num_com_in_sub),se = FALSE)
)


ggplotly(
ggplot(filter(all_pronouns,total_num_com_in_sub > 15, total_num_com_in_sub < 400,num_til_quit > 5)) +
  geom_smooth(aes(post_num,ZNcontains_we, color = "We")) +
  geom_smooth(aes(post_num,ZNcontains_i, color = "I")) +
geom_smooth(aes(post_num,ZNcontains_us,color = "Us"  ))+
geom_smooth(aes(post_num,ZNcontains_they,color = "They"))+
geom_smooth(aes(post_num,ZNcontains_them,color = "Them"))+
geom_smooth(aes(post_num,ZNcontains_you,color = "You" ))+
geom_smooth(aes(post_num,ZNcontains_me,color = "Me"  ))








  
)

ggplotly(
ggplot(filter(all_pronouns,total_num_com_in_sub > 15, total_num_com_in_sub < 400,num_til_quit > 5)) +
  geom_smooth(aes(post_num,contains_we, color = "We")) +
  geom_smooth(aes(post_num,contains_i, color = "I")) 
  
)
```



